The Acoustics Research Group of Brigham Young University analyzed Server room of one of the C7 Data Centers Colocation Lindon 5 Facility in 2010. Results show a signicant amount of sound power below 500Hz and EDT's between 0.25-3.20 seconds depending on location. For more information on C7 Data Centers, check - http://www.c7.com/data-center/

1. Brigham Young University
Acoustical Analysis of Active Control in
the Server Room of a C7 Data Centers
Colocation Facility
Feasibility Report
Advisor:
Dr. Scott Sommerfeldt
C7 Data Centers representative:
Mike Maughan
Group Leaders:
Jesse Daily
James Esplin
Zach Collins
Matthew Shaw
June 21, 2010

2. Contents
1 Executive Summary 2
2 Introduction 2
3 Methods 3
3.1 Data Collection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
Room Acoustic Measurements . . . . . . . . . . . . . . . . . . . . . . . . . . 3
Noise in the Server Room . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
3.2 Computational Simulations . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
Governing Equations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
Computational Model Results . . . . . . . . . . . . . . . . . . . . . . . . . . 8
3.3 Experimental Testing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
4 Conclusion 15
4.1 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
4.2 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
4.3 Recommendation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
5 Contact Information 18
1

3. Feasibility Report
1 Executive Summary
C7 Data Centers provides colocation, virtualization and disaster recovery services and
wanted to explore the possibility of applying active noise control technology to reduce
ambient noise in the data center server room. Active noise control uses loud speakers and
microphones to observe the acoustical environment to emit an “inverse” sound signal to
cancel out the noise. Three methods were used to determine the feasibility of using active
noise control inside the data center server room: measured acoustical data, computational
modeling, and experimental testing.
For the measured data, four measurement locations were considered: inside the data center
entrance door, in front of a C7 Data Centers cage near the center of the room, in a “hot
row” aisle and inside a “cold row” aisle. Two different computational simulations were
performed, the first determined the effect of using a simplified “best-case” scenario model,
and the second used a more complex model. Finally, an experimental test was constructed
to determine if active noise control is feasible for the C7 Data Centers Lindon 5 server
room.
The culmination of our results indicates that active noise control over an extended region
is not possible in the data center room in general. This is due to the random nature of
the noise and the high modal density of the room itself. However, our results indicate that
active noise control may be feasible inside the cold row aisle, given that modest control
(2-4dB) was achieved in laboratory experiments.
2 Introduction
C7 Data Centers has been proactive in seeking out new technologies to make the data
center experience more enjoyable for their customers. Reducing ambient noise on the data
center floor isnt necessarily a need, but more of an interest to see whether something
minimally invasive could be done to counter-act the noise. The C7 Data Centers Lindon
2

4. 5 data center, as is typical of data centers, has little acoustic absorption. The common,
passive forms of acoustical absorption, such as foam or fiberglass panels were not employed
as they contain materials which are highly flammable and give off particulates that could
trigger the early warning fire detection systems.
Active noise control (ANC) was considered as it uses loud speakers and microphones to
observe the acoustical environment and then emits an “inverse” sound signal to attenuate
the noise. Since active control does not use fibrous materials, it may be the ideal solution
for reducing low frequency noise in the server room. To determine if active control is
feasible, acoustic data of the server room was used in concert with computational models
and experimental testing.
3 Methods
Three methods were used to determine the feasibility of using active control in a C7 Data
Centers server room: measured acoustical data, computational modeling, and experimen-
tal testing. Taking acoustical data of the server room yielded valuable information on the
acoustic characteristics of the room and of the noise emitted by the servers and HVAC.
The recorded noise was then used in computational models of the room. These model
simulations were used to estimate the effect that active noise control might have on elim-
inating unwanted sound. Finally, an experimental active control mockup was made to
determine if a physical solution was feasible and to validate or invalidate the computa-
tional model.
3.1 Data Collection
Experimental data of the server room at C7’s Lindon 5 facility was taken to measure the
acoustic characteristics of the room and of the noise emitted by the servers and HVAC.
Four measurement locations were considered (see Fig. 1): by the entrance, in front of a
C7 equipment cage, in a “hot row” and in a “cold row.”
Room Acoustic Measurements
All rooms reverberate sound. As sound reflects off walls and objects within the room, the
sound decays until it cannot be heard. This decay of sound is called the reverberation time
of a room. Reverberation time is found by emitting an impulsive sound (popping a balloon,
firing a starting pistol, etc.) and measuring the time it takes for the sound to decay 60dB
(decibels). This room acoustics measurement is called the T60 time. The Early Decay
3

5. Figure 1: Measurement locations inside the server room.
Time (EDT) is the T60 time calculated by curve fitting the decay curve between 0 and
-10dB.
Reverberation time (or Early Decay Time, EDT) measurements were taken at three loca-
tions (the entrance, the C7 cage and the “cold row”) to determine the acoustic character-
istics of the room. At the entrance to the server room, eight microphones and an energy
density probe were set up to measure the T60 time of the room. A twelve sided loudspeaker
(dodecahedron) was used to ensure that sound was emitted spherically. The dodecahedron
loudspeaker was placed 25’ away from the center of the energy density probe and driven
with a swept sine wave. The Electronic and Acoustic System Evaluation and Response
Analysis (EASERA) was used to measure the EDT for all eight microphones. The micro-
phones were then moved to the “cold row” and the EDT’s were measured again.
Table 1 shows the Early Decay Times (EDT) of the server room as a function of frequency.
The higher the early decay time, the more of a problem a particular frequency range is for
the room. In Table 1, the first two columns show the EDT for the entrance to the server
room with two different source positions (Entrance 1 and Entrance 2). The next two
columns show the remaining EDT data (the C7 Cage and the “Cold Row”).
4

6. Table 1: Early Decay Times (EDT), times in seconds.
Frequency Entrance 1 Entrance 2 C7 Cage Cold Row
File Reference L1-S1-R1 L1-S2-R1 L2-S3-R2 L4-S4-R3
100Hz 1.09 1.48 3.20 0.57
125Hz 1.16 2.28 1.34 0.49
160Hz 1.21 1.28 1.01 0.36
200Hz 1.50 2.25 1.34 0.49
250Hz 1.46 1.55 1.99 0.63
400Hz 2.26 2.06 1.54 0.32
500Hz 1.45 1.73 1.97 0.25
800Hz 1.73 2.23 1.32 0.33
1000Hz 2.06 1.91 1.53 0.37
2000Hz 1.74 1.86 1.37 0.37
4000Hz 1.22 1.10 0.99 0.40
8000Hz 0.54 0.59 0.47 0.31
250-2kHz 1.72 1.89 1.68 0.41
500-4kHz 1.64 1.73 1.50 0.38
An EDT of 3.20 seconds was measured for the 100Hz band by the C7 cage, which indicates
that 100Hz is a reverberant frequency for the C7 Data Centers server room. At 8000Hz,
all of the EDT’s are below 0.6 seconds, meaning that those frequencies are not highly
reverberant.
Noise in the Server Room
Noise is generally defined as unwanted sound. In C7 Data Centers server room, noise can
be defined as the sounds emitted by the many computer fans and HVAC in the server room.
These sources emit sound over a wide range of frequencies due to the turbulent air flow
produced by the fans. Since fans operate at discrete frequencies, certain frequencies have
more power than others. Eliminating these frequencies would greatly reduce the amount
of noise present in the server room.
Measurements of the noise in the server room were taken at three locations. These locations
were chosen for being high foot traffic areas for both technicians and visitors to the data
center. The first location was in front of a C7 equipment cage in the main aisle of the
server room. The second location was in a “hot row”, an aisle between cabinet rows where
the hot air coming out of the servers is directed. Lastly, noise was also recorded in a
“cold row” (see figure 3), an enclosed aisle between cabinet rows that contains the cold air
and directs it into the server cabinets. Figure 2 shows the sound power recorded at the
5

7. Figure 2: Power spectra of unfiltered C7 Data Center server
room noise
Figure 3: Measurements taken
from inside the “cold row”
entrance to C7’s Lindon 5 facility server room. These noise data were later used as input
for computational models and experimental testing.
3.2 Computational Simulations
The server room can be computationally modeled to determine the feasibility of using active
noise control. This model can be used not only to determine the feasibility of active noise
control but also to determine ideal locations and configurations for speaker installation.
The model will consist of ‘primary sources’ emitting the recorded noise taken in the C7
Data Centers Lindon 5 server room and ‘control sources’ trying to actively control the
noise.
Governing Equations
The computational model is based on fundamental acoustic principles. The server noise
was approximated by several point sources emitting fan noise. The wave equation models
the emission of sound pressure waves with a point source that can be defined as
6

8. 2
ˆp −
1
c2
∂2 ˆp
∂t2
= −Q0(t)δ(r − r0), (1)
where Q0(t) is the volume velocity of the point source. The pressure ˆp is the pressure field
in the volume and can be characterized as the sumed response of all the point sources.
ˆp =
Nmax
N=1
qN ψN (2)
The variable qn represents all n point sources that must be solved in the matrix,



(k2
0 − k2
1)C11 + D11 D12 · · ·
D21 (k2
0 − k2
2)C22 + D22 · · ·
...
...
...


 ·



q1
q2
...


 =



− ˆQ0ψ∗
1(r0)
− ˆQ0ψ∗
2(r0)
...



or
A · Q = B, (3)
where ki is the acoustic wave number for each source and
Cmn =
V
ψ∗
mψnd3
x = Λmnδmn (4)
Dmn =
s
(β − βI
)ψ∗
mψnda (5)
Referring back to Eq. (2), the variable ψn represents the eigenfunction solutions in Carte-
sian coordinates which are defined as,
ψN = cos
mπ
Lx
x cos
nπ
Ly
y cos
lπ
Lz
z . (6)
The variables Lx, Ly, Lz are the dimensions of the room.
In Eq. (3), the A matrix can become very large. As the A matrix grows, solving for
the qN ’s becomes increasingly difficult. To simplify the solution process, the off-diagonal
terms in the A matrix can be deleted, effectively decoupling the A matrix and making the
matrix far easier to solve. This code has been used previously with success for smaller
rooms.
7

9. Figure 4: Attenuation of the noise in the “cold row” using the root-sum-squared technique.
Computational Model Results
Two different computational simulations were performed. The first was to determine the
effect of using a “best-case” scenario. The second model created was a more reasonable
control method that used a more complex model.
The first simulation was performed to estimate the maximum achievable potential energy
reduction in the “cold rows” of the server room. The dimensions of the simulated “cold
row” were 10.4×3.4×2.4m. Fifteen thousand modes were used, and control was simulated
every 20Hz from 100Hz to 1kHz. The disturbance sound field was simulated by a uniform
random distribution of 100 sources throughout the “cold row”, each with random phase
and amplitude. Seven control sources were used, the first at the center of the room. The
other six were 1m from the center of the room in the shape of a cube. The control field for
each case was simulated and recorded.
The optimum controller was found for each speaker by minimizing the sum of squared
pressures at 36 simulated locations, uniformly distributed around the center of the “cold
row.” The covered region extended from the center approximately 1m each direction in
length, 0.55m each direction in height, and 0.6m each direction in width. The dB atten-
uation spectrum achieved using control speakers one-at-a-time was recorded. The seven
attenuation spectra were combined using a root-sum-squared averaging technique. The
resulting attenuation achieved over the frequency range studied is shown in Fig. 4.
8

10. Figure 5: Attenuation of the noise in the “cold row” using global potential energy.
As can be seen, control effects in the range of 2-6dB are achievable below 200Hz. As
frequency increases, attenuation remains steady at 1dB. This level of control required 7
simulated speakers and 36 simulated microphones. While this is infeasible in real life, it
does give some idea of the limits of achievable attenuation.
Next, the “cold row” was modeled again by controlling the global potential energy through-
out the entire “cold row” (see Fig. 5).
Global levels drop by 1-3dB below 200Hz, and about 0.5dB above 200Hz. While this
indicates that large-area control is infeasible, it also shows that spillover problems are not
likely very serious.
For the second computational model, a diffuse sound field was created in a room with
slightly absorbent walls. All four previously mentioned locations within the data center
were considered, each at a number of different frequencies. Table 2 shows the frequencies
tested at each location:
For locations 1-3, the size of the room was 28 × 31.7 × 4.1m. The size of the room at
location 4 was 10.4 × 3.3 × 2.4m.
For each measurement location and frequency, a variety of different control methods were
attempted. The number of primary noise sources varied between 1 and 3 while the sec-
ondary control sources were varied between 1 and 6 sources.
For each control method, the attenuated pressure field was found at three orthogonal
9

11. Table 2: Frequencies tested at each location.
Entrance C7 cage Hot row Cold row
356Hz 360Hz 305Hz 360Hz
500Hz 457Hz 455Hz 457Hz
540Hz 540Hz
588Hz
Figure 6: 360Hz, 1 primary source, 1 control source in the x, y and z planes.
planes cutting through the sensor. The attenuated field was found by minimizing the
squared pressure at the sensors and by minimizing the energy density at the sensors (see
Figs. 6 - 8, positive values indicate attenuation). The circles in the figures indicate error
sensor locations.
By controlling squared pressure, the field directly around the sensors is attenuated dra-
matically, but there is not a large region of control. When energy density is minimized at
the sensors, there is more of a global attenuation, but there are still many locations where
the sound level is boosted.
Preliminary runs of the model were not encouraging. After 80 hours of computation, none
of the models had produced any results. This was due to the fact that the solver was
unable to converge and solve the large matrices in the code. To eliminate this problem,
the code was changed to uncouple the point sources. This uncoupling allowed the model
to solve for most frequencies (lower frequencies were more likely to converge).
10

12. Figure 7: 457Hz, 2 primary sources, 2 control sources in the x, y and z planes.
Figure 8: 540Hz, 3 primary sources, 6 control sources in the x, y and z planes.
3.3 Experimental Testing
Methods
An experimental test was constructed to determine if active noise control was feasible for
C7 Data Centers Lindon 5 server room. There are two possible locations for employing
ANC in C7’s Lindon 5 server room: the server room in general, and the cold rows. Ideally,
the experimental ANC would be conducted in rooms of similar dimensions and room rever-
beration times for both the server room and the cold rows. However, rooms with similar
characteristics without extraneous noise were not available at BYU. For simplicity, normal
classes and labs were used to test ANC. While these experimental results were not identical
11

13. to those that would be produced in the actual locations, the results yielded valuable insight
into whether or not ANC was possible.
The first step in determining if ANC is possible was to try to control a simple sine wave.
As a sine wave only has a single frequency, it is the easiest signal to control. If the
ANC failed to control a sine wave then there was little possibility of controlling a more
complex signal such as white noise. The experiment was conducted in U186C in the Eyring
Science Center at Brigham Young University (room dimensions ≈ 4m x 4m x 4m). The
experimental setup consisted of two Mackie loudspeakers. The first loudspeaker acted as
the source, while the second loudspeaker was used as the control. A two-dimensional, four
channel energy density sensor (a sensor that measures both pressure and velocity instead
of just pressure) was used as the error sensor. A 1
4 microphone was used to monitor the
effectiveness of ANC. The microphone was moved away from the reference energy density
sensor to measure how ANC performed as a function of distance.
Results
A sine wave was first controlled to ensure the ANC system was functioning correctly. Two
frequencies were controlled: 100Hz and 250Hz, and the resulting pressure was monitored
at distances of 12.7cm, 25.4cm, 50.8cm, and 121.92cm from the error sensor. The results
can be seen in Table 3. For the 100Hz tone at 12.7cm, active noise control attenuates the
fundamental tone nearly 30dB, and the third harmonic by 20dB. The 100Hz tone at 25.4cm
was attenuated 10dB for the fundamental but was boosted 10dB for the third harmonic.
The overall sound pressure level of each of these tests can be seen in Figs. 9 and 10. For
the 100Hz tone, ANC was able to attenuate the overall signal out to a distance of about
40cm (see Fig. 9). However, for the 250Hz signal, ANC was only able to attenuate the
source signal out to a distance of 20cm before the signal is actually boosted by as much as
4dB (see Fig. 10). These results are typical of what can be expected for ANC in a room
of similar dimensions.
12

14. Figure 9: Overall sound pressure level for 100Hz sine wave as a function of distance.
Figure 10: Overall sound pressure level for 250Hz sine wave as a function of distance.
The noise from the server room was then output as the source to be controlled. The noise
was controlled at distances of 1cm, 12.7cm, 25.4cm, 50.8cm, and 121.92cm. The results
can be seen in Fig. 11. It can be seen that for all distances, ANC was able to attenuate
the main tone at 40Hz by several dB. Above 100Hz ANC contributes very little, neither
attenuating nor boosting the signal. These results were then processed to visualize the
13

15. Table 3: ANC results for sine waves at 100Hz and 250Hz. Results measured at 12.7cm,
25.4cm, 50.8cm, and 121.92cm.
100Hz 250Hz
12.7cm26.4cm50.8cm121.92cm
overall sound pressure level (see Fig. 12). The ANC results for the noise from the server
room were promising: 2-4dB of attenuation was achieved out to a distance of 120cm (see
Fig. 11). Three comments need to be made about these results.
1. The experimental setup used only a single source to output the recorded noise from
14

16. the server room. In reality, there will be many sources of noise in the server room
and in the “cold rows.”
2. The experimental setup used only one control source to implement ANC in the room.
Since this was just an experimental mockup, only one control source was attempted.
If ANC were implemented in the server room, multiple control sources could be used
to perform ANC.
3. The aspect ratio of the test room was approximately one. This means that the modal
density in the room increases rapidly with the room dimensions. Rooms having high
modal densities are more difficult to control. The “cold rows” have a much higher
aspect ratio (long and thin), meaning that they will have fewer modes present in the
room.
4 Conclusion
4.1 Summary
Three aspects of engineering were considered in characterizing the C7 Data Centers Lindon
5 server room noise: taking experimental data, running computational simulations, and
mockup testing. The first step was taking the acoustic data present at the server room.
These data were crucial in determining what frequencies contained the most energy and
were integral for use in the computational models and the experimental mockups. Data
were taken in 4 locations in the C7 Data Centers Lindon 5 server room, including data
that were taken in the “cold rows.”
Computational simulations were performed to model the use of active noise control in the
server room. The models used were originally designed by Buye Xu for modeling modal
analysis in an enclosed 3-D space. This model was used to simulate both the “cold rows”
and the entire server room. Initially, the solvers in the code struggled to converge. The
codes were modified to decouple all the point sources, which makes the simulations less real-
istic, but simplified the matrices generated during the simulation. Multiple frequencies were
tested with multiple control sources. Results were not encouraging. Computational results
show that almost no control is possible in the server room or in the “cold rows.”
Finally, an experimental mockup was set up to attempt active noise control in a controlled
laboratory setting. A sine wave was output as a trial signal to test the active noise control
system. Once a sine wave was controlled in a laboratory setting, the recorded noise from the
C7 Data Centers Lindon 5 server room was output through a loudspeaker. This recorded
signal was then controlled in the lab using ANC with some attenuation achieved.
15

17. Figure 11: ANC results for server room noise. Results measured at 1cm, 12.7cm, 25.4cm,
50.8cm, and 121.92cm.
16

18. Figure 12: Overall sound pressure level for server room noise as a function of distance.
4.2 Conclusions
Data were taken at C7 Data centers Lindon 5 facility. These were used to calculate the
early decay times of the main server room and the “cold rows.” Results show a significant
amount of sound power below 500Hz and EDT’s between 0.25-3.20 seconds depending on
location. These data were also used in both the computational models and the experimental
testing of active noise control.
While useful, the computational model had some severe drawbacks. The rooms input into
this model were on the order of a few meters to several dozen meters. The code was run
on BYU’s supercomputer, Mary Lou, but after 80 hours of computation time, none of
the coupled codes were able to run to completion. Only after decoupling the code were
a few of the codes able to run to completion for a few select frequencies. The results of
these simulations indicate that ANC is not effective. However, these results are not in full
agreement with the results of the experimental testing done in the lab.
The experimental testing done using the measurements from the C7 Data Centers Lindon
5 server room indicate that active noise control is able to attenuate the noise by 2-4dB
and may therefore be a viable option. These results were verified by first controlling a
simple sine wave. Given the difficulties encountered with the computational code, the
experimental testing is considered to yield the more reliable results.
17

19. 4.3 Recommendation
The initial plan for implementing active noise control in the C7 Data Centers Lindon 5
facility was at two locations, in the server room, and in the “cold rows.” The cumulation
of our results indicates that active noise control will not be effective in the main area of
the server room (this includes the Entrance and the C7 Cage area). This is due to the
random nature of the noise and the high modal density present in the main room. However
our results indicate that active noise control may be possible in the “cold rows.” Since
active noise control was possible in the laboratory experiments, it can be said that similar
control within the “cold rows” may be feasible, but more elaborate experiments should be
performed to ensure that active noise control can yield sufficient acoustic control.
5 Contact Information
Dr. Scott Sommerfeldt 1
Dean, College of Physical and Mathematical Sciences
phone: 801.422.2205
email: scott sommerfeldt@byu.edu
Jesse Daily
Graduate student
phone: 925.321.4815
email: dai01001@gmail.com
Mike Maughan
C7 Data Centers
email: mikem@c7dc.com
Contributing Students: Zach Collins, Jesse Daily, James Esplin, Jarom Giraud, David
Krueger, Dan Manwill, Matt Shaw, Brad Solomon, Dan Tengelsen, Alan Wall, Buye
Xu
1
Corresponding author
18